Embodiments herein generally relate to detection and discrimination of rhythm patterns of interest, and more particularly to discriminating Tachy-Brady Syndrome episodes from atrial fibrillation episodes.
Atrial fibrillation (AF) is a common and serious cardiac arrhythmia, affecting more than two million people in the United States alone. Clinically, atrial fibrillation involves an abnormality of electrical impulse formation and conduction that originates in the atria. Atrial fibrillation is characterized by multiple swirling wavelets of electrical current spreading across the atria in a disorganized manner. The irregularity of electrical conduction throughout the atria creates irregular impulse propagation through the atrioventricular (AV) node into the ventricle.
Impulse propagation through the AV node may be extremely rapid, leading to reduced diastolic filling of the heart chambers and a corresponding reduction of the cardiac pumping action. Increased heart rate and loss of AV synchrony may also exacerbate any underlying heart problems, such as heart failure, coronary blood flow, or other pulmonary disorders. Alternatively, impulse propagation through the AV node may be very limited due to AV node refractoriness so that atrial fibrillation can be sustained indefinitely, since the ventricles continue to drive circulation, albeit inefficiently.
AF monitoring systems have been developed for use in an ambulatory setting, which may be either external, such as a Holter monitor, or internal, such as implantable cardiac monitors or “loop recorders”. These systems continually sense cardiac electrical signals from a patient's heart, process the signals to detect AF and upon detection, record the electrical signals for subsequent review and analysis by a care provider.
More recently, interest has increased in providing improved implantable cardiac monitors. It has been proposed that implantable cardiac monitors may be used for diagnosis of re-current AF after surgical AF ablation, catheter AF ablation, atrial fibrillation ablation and cryptogenic stroke. Further, there is an interest in managing AF episodes in connection with medication usage, as well as monitoring AF in connection with detecting periodic atrial cardioversion.
However, existing algorithms used by monitoring systems for detecting AF are primarily based on the irregularity of R-waves, since the system uses only 2 electrodes. Due to the difficulty in detecting P-waves, these systems may provide false positives, and declare AF detection, when AF did not necessarily exist. As one example, certain AF detection algorithms may be confused when a patient exhibits irregular rhythms that are not AF episodes. Since the monitoring system does not detect P waves, when a clinician views stored electrocardiogram data, the physician needs to analyze the rhythm in an effort to observe where sinus beats or other aberrations are present.
Further, existing AF detection algorithms may experience undue false positives in connection with certain irregular rhythm patterns. Existing AF algorithms may not exhibit sufficient positive predictive value (PPV) of AF episode detection and duration (burden). Heretofore, it has been proposed to utilize “P-wave evidence”, in connection with AF detection algorithms, in an effort to reduce the false positives declared by AF detection algorithms. In general, P-wave evidence related algorithms look backwards in time through an ECG signal for the presence of P-waves and discard the data when evidence of P waves is present. However, P-wave evidence-based algorithms may still exhibit false positives and may not be effective in all circumstances.
Therefore, a need remains for improved methods and systems for discriminating AF detection and reducing false detection of atrial fibrillation.